A comparative assessment of bagging ensemble models for modeling concrete slump flow

Aydoğmuş, Hacer Yumurtacı; Erdal, Halil İbrahim; Karakurt, Onur; Namlı, Ersin; Türkan, Yusuf Sait; Erdal, Hami̇t

doi:10.12989/cac.2015.16.5.741

Cited by 39 publications

(10 citation statements)

References 25 publications

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“…Ensemble models outclass individual algorithms to predict chloride penetration in RC. Hacer et al [ 36 ] present the comparative assessment of bagging as the ensemble approach for high-performance concrete mix slump flow. Ensemble models with bagging were found to be superior with regard to standalone approaches.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

et al. 2021

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Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

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Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

et al. 2021

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“…After performing feature selection, the reduced feature dataset is given as an input to the bagging ensemble model classifier. In particular, a bagging ensemble model is chosen as it can reduce the classification variance of any base model while maintaining their low bias characteristics and improving the classification accuracy [34]. Therefore, a bagging ensemble classification model is adopted in this work with the aim of providing a domain typo-squatting detection framework with high accuracy and low false alarm rate.…”

Section: B Proposed Approach Applicationmentioning

confidence: 99%

Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection

Moubayed¹,

Aqeeli²,

Shami³

2020

Preprint

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Domain Name System (DNS) plays in important role in the current IP-based Internet architecture. This is because it performs the domain name to IP resolution. However, the DNS protocol has several security vulnerabilities due to the lack of data integrity and origin authentication within it. This paper focuses on one particular security vulnerability, namely typo-squatting. Typo-squatting refers to the registration of a domain name that is extremely similar to that of an existing popular brand with the goal of redirecting users to malicious/suspicious websites. The danger of typo-squatting is that it can lead to information threat, corporate secret leakage, and can facilitate fraud. This paper builds on our previous work in [1], which only proposed majorityvoting based classifier, by proposing an ensemble-based feature selection and bagging classification model to detect DNS typosquatting attack. Experimental results show that the proposed framework achieves high accuracy and precision in identifying the malicious/suspicious typo-squatting domains (a loss of at most 1.5% in accuracy and 5% in precision when compared to the model that used the complete feature set) while having a lower computational complexity due to the smaller feature set (a reduction of more than 50% in feature set size).

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“…The prediction in the regression is achieved through averaging while in classification; it is attained through majority vote [42], [44]. The bagging meta-algorithm is however best suited in base models that are characterized by underfitting and overfitting due to its ability to reduce the base model variance through averaging or voting without doing any significant change on the bias and high Experimentation by various researchers has established that bagging ensemble to be quite superior to their associated base models [47], [48], [49]. The researchers continue to assert that the ability of bagging to minimize prediction errors as well as optimize prediction accuracy can be utilized in High-Performance Concrete (HPC) slump flow modeling to attain high accuracy [47].…”

Section: How Bagging Workmentioning

confidence: 99%

“…The bagging meta-algorithm is however best suited in base models that are characterized by underfitting and overfitting due to its ability to reduce the base model variance through averaging or voting without doing any significant change on the bias and high Experimentation by various researchers has established that bagging ensemble to be quite superior to their associated base models [47], [48], [49]. The researchers continue to assert that the ability of bagging to minimize prediction errors as well as optimize prediction accuracy can be utilized in High-Performance Concrete (HPC) slump flow modeling to attain high accuracy [47]. As identified by the researchers, HPC remains a complex field that can potentially benefit from bagging ensembles, as the latter is often associated with significant noise in the dataset [47].…”

Section: How Bagging Workmentioning

confidence: 99%

“…The researchers continue to assert that the ability of bagging to minimize prediction errors as well as optimize prediction accuracy can be utilized in High-Performance Concrete (HPC) slump flow modeling to attain high accuracy [47]. As identified by the researchers, HPC remains a complex field that can potentially benefit from bagging ensembles, as the latter is often associated with significant noise in the dataset [47]. In the banking industry, Erdal and Karahanoğlu experimented the variants of bagging and specifically Reduced Error Pruning Tree (REPTree), Random Tree (RTree) and Decision Stump (DStump) on financial data and concluded that bagging could be potentially used for prediction of the Turkish Development and Investment Banks profitability determinants [49].…”

Section: How Bagging Workmentioning

confidence: 99%

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The Superiority of the Ensemble Classification Methods: A Comprehensive Review

Nzuva¹,

Nderu²

2019

JIEA

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The modern technologies, which are characterized by cyber-physical systems and internet of things expose organizations to big data, which in turn can be processed to derive actionable knowledge. Machine learning techniques have vastly been employed in both supervised and unsupervised environments in an effort to develop systems that are capable of making feasible decisions in light of past data. In order to enhance the accuracy of supervised learning algorithms, various classification-based ensemble methods have been developed. Herein, we review the superiority exhibited by ensemble learning algorithms based on the past that has been carried out over the years. Moreover, we proceed to compare and discuss the common classification-based ensemble methods, with an emphasis on the boosting and bagging ensemble-learning models. We conclude by out setting the superiority of the ensemble learning models over individual base learners.

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A comparative assessment of bagging ensemble models for modeling concrete slump flow

Cited by 39 publications

References 25 publications

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Ensemble-based Feature Selection and Classification Model for DNS Typo-squatting Detection

The Superiority of the Ensemble Classification Methods: A Comprehensive Review

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